In general, automation of an appearance inspection is has been increasingly demanded in an appearance inspection process and the like in production sites. As an automation technique, a technique of discriminating non-defective products from defective products by a discriminator defined in advance using video/image data obtained by signal obtainment using a line sensor or an area sensor, for example, has been used.
However, when the discriminator is used in the appearance inspection, the discriminator is required to be defined in advance. To define the discriminator, the discriminator is appropriately controlled. The control of the discriminator is often performed when a production line is installed. However, learning data may not be sufficient since an amount of learning data is small for generation of the discriminator or data of defective products is little at the time of the installation.
NPL 1 discloses a method referred to as “transfer learning” as a technique of learning a discriminator of high accuracy even when an amount of data is small in a field of machine learning. The transfer learning is performed to efficiently find an effective hypothesis of a new task by applying knowledge obtained by learning in at least another task. Specifically, in the transfer learning, data is stored in advance, new similar data is compared with the data stored in the past, and a new discriminator is defined.